You will find below the "TRAIL researcher's charter" with a version in French and another in English.

Vous trouverez ci-dessous la "Charte du chercheur TRAIL" avec une version en français et une autre en anglais.

Background subtraction (BGS) is a common choice for performing motion detection in video. Hundreds of BGS algorithms are released every year, but combining them to detect motion remains largely unexplored. We found that combination strategies allow to capitalize on this massive amount of available BGS algorithms, and offer significant space for performance improvement. In this paper, we explore sets of performances achievable by 6 strategies combining, pixelwise, the outputs of 26 unsupervised BGS algorithms, on the CDnet 2014 dataset, both in the ROC space and in terms of the F1 score.

Contemporary social coding platforms such as GitHub facilitate collaborative distributed software development. Developers engaged in these platforms often use machine accounts (bots) for automating effort-intensive or repetitive activities. Determining whether a contributor corresponds to a bot or a human account is important in socio-technical studies, for example, to assess the positive and negative impact of using bots, analyse the evolution of bots and their usage, identify top human contributors, and so on.

It is a commonly accepted fact that machine learning requires large amounts of data. Fortunately, the sources of information are more and more numerous and the amount of data available in all domains is constantly increasing. However, this evolution has reached a point where it is no longer realistic to think of storing the whole set of data needed for a machine learning task on a single computer. This has led J. Konecny, H.B. MacMahan and D. Ramage to propose a new learning model in which the data is scattered on distributed nodes and the model is learned in a distributed manner.

Objectives:

Enhance the trustworthiness of nonlinear dimensionality reduction algorithms by developing explainability methods for low-dimensional (typically 2 or 3-D) nonlinear embeddings, as analyzed in the context of exploratory data visualization tasks.

Research RoadMap:

- Review of the state of the art: current explainability techniques for nonlinear embeddings.
- Identification of their limitations.
- Design of an explainability framework to address them.

Objectives:

Détection de biais et/ou mitigation de biais dans les jeux de données (images ou texte).

Research RoadMap:

- Analyse de l'existant : types de biais
- Sélection des types de biais qu'on souhaite détecter/mitiger.
- Mise à disposition d'une checklist pour détecter des biais initialement présent dans un jeu de données.

Objectif du défi

L'objectif est de créer et de valider des solutions d'IA qui réduisent radicalement le besoin d'énormes quantités de données étiquetées et démontrent de fortes capacités de généralisation pour permettre aux entreprises disposant de moins de données étiquetées de présenter des solutions d'IA compétitives. par rapport aux principaux acteurs (GAFAM)

Objectif du défi

L'objectif de ce défi est de construire et d'évaluer une IA digne de confiance et de réduire ainsi les obstacles pour les entreprises wallonnes désireuses de déployer des solutions d'IA dans des secteurs réglementés.